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Unified Batch and Real- Time Stream Processing Using Apache Flink Slim Baltagi Director of Big Data Engineering Capital One September 15, 2015 Washington DC Area Apache Flink Meetup

Unified Batch and Real-Time Stream Processing Using Apache Flink

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Page 1: Unified Batch and Real-Time Stream Processing Using Apache Flink

Unified Batch and Real-Time Stream Processing Using Apache Flink

Slim BaltagiDirector of Big Data EngineeringCapital One

September 15, 2015

Washington DC Area Apache Flink Meetup

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Agenda

1. What is Apache Flink?2. Why Apache Flink? 3. How Apache Flink is used at Capital

One?4. Where to learn more about Apache

Flink?5. What are some key takeaways?

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1. What is Apache Flink? Apache Flink, like Apache Hadoop and Apache

Spark, is a community-driven open source framework for distributed Big Data Analytics.

Apache Flink has its origins in a research project called Stratosphere started in 2009 at the Technische Universität Berlin in Germany.

In German, Flink means agile or swift. Flink joined the Apache incubator in April 2014 and

graduated as an Apache Top Level Project (TLP) in December 2014 (the fastest Apache project to do so!)

DataArtisans (data-artisans.com) is a German start-up company leading the development of Apache Flink.

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What is a typical Big Data Analytics Stack: Hadoop, Spark, Flink, …?

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1. What is Apache Flink? Now, with all the buzz about Apache Spark, where

Apache Flink fits in the Big Data ecosystem and why do we need Flink!?

Apache Flink is not YABDAF (Yet Another Big Data Analytics Framework)!

Flink brings many technical innovations and a unique vision and philosophy that distinguish it from: Other multi-purpose Big Data analytics frameworks

such as Apache Hadoop and Apache Spark Single-purpose Big Data Analytics frameworks such

as Apache Storm

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• Declarativity• Query optimization• Efficient parallel in-

memory and out-of-core algorithms

• Massive scale-out• User Defined

Functions • Complex data types• Schema on read

• Real-Time Streaming

• Iterations• Memory

Management• Advanced

Dataflows• General APIs

Draws on concepts from

MPP Database Technology

Draws on concepts from

Hadoop MapReduce Technology

Add

1. What is Apache Flink? hat are the principles on which Flink is built on?

Apache Flink’s original vision was getting the best from both worlds: MPP Technology and Hadoop MapReduce Technologies:

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What is Apache Flink stack?

Gel

lyTa

ble

Had

oop

M/R

SAM

OA

DataSet (Java/Scala/Python)Batch Processing

DataStream (Java/Scala)Stream Processing

Flin

kML

LocalSingle JVMEmbedded

Docker

ClusterStandalone YARN, Tez, Mesos (WIP)

CloudGoogle’s GCEAmazon’s EC2IBM Docker Cloud, …

Goo

gle

Dat

aflo

w

Dat

aflo

w (W

iP)

MR

QL

Tabl

e

Cas

cadi

ng

Runtime - Distributed Streaming Dataflow

Zepp

elin

DEP

LOY

SYST

EMA

PIs

& L

IBR

AR

IES

STO

RA

GE Files

LocalHDFS

S3, Azure StorageTachyon

DatabasesMongoDB HBaseSQL …

Streams FlumeKafkaRabbitMQ…

Batch Optimizer Stream Builder

Stor

m

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1. What is Apache Flink?The core of Flink is a distributed and scalable streaming dataflow engine with some unique features:

1. True streaming capabilities: Execute everything as streams

2. Native iterative execution: Allow some cyclic dataflows

3. Handling of mutable state4. Custom memory manager: Operate on managed

memory5. Cost-Based Optimizer: for both batch and stream

processing

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1. What is Apache Flink? hat are the principles on which Flink is built on?

1. Get the best from both worlds: MPP Technology and Hadoop MapReduce Technologies. 2. All streaming all the time: execute everything as streams including batch!!3. Write like a programming language, execute like a database.4. Alleviate the user from a lot of the pain of:

manually tuning memory assignment to intermediate operators

dealing with physical execution concepts (e.g., choosing between broadcast and partitioned joins, reusing partitions)

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1. What is Apache Flink? n?

5. Little configuration required Requires no memory thresholds to configure –

Flink manages its own memory Requires no complicated network configurations –

Pipelining engine requires much less memory for data exchange

Requires no serializers to be configured – Flink handles its own type extraction and data representation

6. Little tuning required: Programs can be adjusted to data automatically – Flink’s optimizer can choose execution strategies automatically

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21. What is Apache Flink? n. What are the principles on which Flink is built on?

7. Support for many file systems: Flink is File System agnostic. BYOS: Bring Your

Own Storage8. Support for many deployment options:

Flink is agnostic to the underlying cluster infrastructure.. BYOC: Bring Your Own Cluster

9. Be a good citizen of the Hadoop ecosystemGood integration with YARN and Tez

10. Preserve your investment in your legacy Big Data applications: Run your legacy code on Flink’s powerful engine using Hadoop and Storm compatibilities layers and Cascading adapter.

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1. What is Apache Flink? n?

11. Native Support of many use cases: Batch, real-time streaming, machine learning,

graph processing, relational queries on top of the same streaming engine.

Support building complex data pipelines leveraging native libraries without the need to combine and manage external ones.

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Agenda

1. What is Apache Flink?2. Why Apache Flink? 3. How Apache Flink is used at Capital

One?4. Where to learn more about Apache

Flink?5. What are some key takeaways?

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2. Why Apache Flink? Apache Flink is uniquely positioned at the

forefront of the following major trends in the Big Data Analytics frameworks:

1. Unification of Batch and Stream Processing2. Multi-purpose Big Data analytics

frameworksApache Flink is leading the movement of

stream processing-first in the open source.Apache Flink can be considered the 4G of the

Big Data Analytics Frameworks.

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2. Why Apache Flink? - The 4G of Big Data Analytics FrameworksBig Data Analytics engines evolved?

Batch Batch Interactive

Hybrid(Streaming +Batch) Interactive Near-Real Time

Streaming Iterative

processing In-Memory

Hybrid(Streaming +Batch) Interactive Real-Time

Streaming Native Iterative

processing In-Memory

MapReduce Direct Acyclic Graphs (DAG)Dataflows

RDD: Resilient Distributed Datasets

Cyclic Dataflows

1G 2G 3G 4G

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2. Why Apache Flink? - The 4G of Stream Processing Toolsengineeolved?

Single-purpose

Runs in a separate non-Hadoop cluster

Single-purpose

Runs in the same Hadoop cluster via YARN

Hybrid (Streaming +Batch)

Built for batch

Models streams as micro-batches

Hybrid(Streaming +Batch) Built for

streaming Models

batches as finite data streams

1G 2G 3G 4G

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2. Why Apache Flink? – Good integration with the Hadoop ecosystem

Flink integrates well with other open source tools for data input and output as well as deployment. 

Hadoop integration out of the box: HDFS to read and write. Secure HDFS supportDeploy inside of Hadoop via YARNReuse data types (that implement Writables

interface) YARN Setup http://ci.apache.org/projects/flink/flink-docs-master/setup/

yarn_setup.html

YARN Configurationhttp://ci.apache.org/projects/flink/flink-docs-master/setup/config.html#yarn

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2. Why Apache Flink? – Good integration with the Hadoop ecosystem

Hadoop Compatibility in Flink by Fabian Hüske - November 18, 2014 http://flink.apache.org/news/2014/11/18/hadoop-compatibility.html

Hadoop integration with a thin wrapper (Hadoop Compatibility layer) to run legacy Hadoop MapReduce jobs, reuse Hadoop input and output formats and reuse functions like Map and Reduce. https://ci.apache.org/projects/flink/flink-docs-master/apis/hadoop_compatibility.html

Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm.

https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html

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2. Why Apache Flink? – Good integration with the Hadoop ecosystem

Service Open Source ToolStorage/Serving Layer

Data Formats

Data Ingestion Services

Resource Management

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2. Why Apache Flink? – Good integration with the Hadoop ecosystemApache Bigtop (Work-In-Progress) http://bigtop.apache.orgHere are some examples of how to read/write data

from/to HBase:  https

://github.com/apache/flink/tree/master/flink-staging/flink-hbase/src/test/java/org/apache/flink/addons/hbase/example

Using Kafka with Flink: https://ci.apache.org/projects/flink/flink-docs-master/apis/ streaming_guide.html#apache-kafka

Using MongoDB with Flink: http://flink.apache.org/news/2014/01/28/querying_mongodb.html

Amazon S3, Microsoft Azure Storage

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2. Why Apache Flink? – Good integration with the Hadoop ecosystem

Apache Flink + Apache SAMOA for Machine Learning on streams http://samoa.incubator.apache.org/

Flink Integrates with Zeppelin http://zeppelin.incubator.apache.org/

Flink on Apache Tez http://tez.apache.org/

Flink + Apache MRQL http://mrql.incubator.apache.org

Flink + Tachyon http://tachyon-project.org/

Running Apache Flink on Tachyon http://tachyon-project.org/Running-Flink-on-Tachyon.html

Flink + XtreemFS http://www.xtreemfs.org/

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2. Why Apache Flink? - Unification of Batch & Streaming Many big data sources represent series of events that

are continuously produced. Example: tweets, web logs, user transactions, system logs, sensor networks, …

Batch processing:  These events are collected together for a certain period of time (a day for example) and stored somewhere to be processed as a finite data set.

What’s the problem with ‘process-after-store’ model: Unnecessary latencies between data generation and

analysis & actions on the data. Implicit assumption that the data is complete after a

given period of time and can be used to make accurate predictions.

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2. Why Apache Flink? - Unification of Batch & Streaming Many applications must continuously receive large

streams of live data, process them and provide results in real-time. Real-Time means business time!

A typical design pattern in streaming architecturehttp://www.kdnuggets.com/2015/08/apache-flink-stream-processing.html

The 8 Requirements of Real-Time Stream Processing, Stonebraker et al. 2005 http://blog.acolyer.org/2014/12/03/the-8-requirements-of-real-time-stream-processing/

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2. Why Apache Flink? - Unification of Batch & Streaming

case class Word (word: String, frequency: Int)

val env = StreamExecutionEnvironment.getExecutionEnvironment()val lines: DataStream[String] = env.fromSocketStream(...)lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS)) .groupBy("word").sum("frequency") .print()env.execute()

val env = ExecutionEnvironment.getExecutionEnvironment()val lines: DataSet[String] = env.readTextFile(...)lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .groupBy("word”).sum("frequency") .print()env.execute()

DataSet API (batch): WordCount

DataStream API (streaming): Window WordCount

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2. Why Apache Flink? - Unification of Batch & Streaming

Google Cloud Dataflow (GA on August 12, 2015) is a fully-managed cloud service and a unified programming model for batch and streaming big data processing.https://cloud.google.com/dataflow/ (Try it FREE)http://goo.gl/2aYsl0

Flink-Dataflow is a Google Cloud Dataflow SDK Runner for Apache Flink. It enables you to run Dataflow programs with Flink as an execution engine.

The integration is done with the open APIs provided by Google Data Flow.

Support for Flink DataStream API is Work in Progress

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2. Why Apache Flink? - Unification of Batch & Streaming

Unification of Batch and Stream Processing: In Lambda Architecture: Two separate execution

engines for batch and streaming as in the Hadoop ecosystem (MapReduce + Apache Storm) or Google Dataflow (FlumeJava + MillWheel) …

In Kappa Architecture: a single hybrid engine (Real-Time stream processing + Batch processing) where every workload is executed as streams including batch!

Flink implements the Kappa Architecture: run batch programs on a streaming system.

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2. Why Apache Flink? - Unification of Batch & Streaming

References about the Kappa Architecture: Batch is a special case of streaming- Apache Flink

and the Kappa Architecture - Kostas Tzoumas, September 2015.http://data-artisans.com/batch-is-a-special-case-of-streaming/

Questioning the Lambda Architecture - Jay Kreps , July 2nd, 2014 http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html

Turning the database inside out with Apache Samza -Martin Kleppmann, March 4th, 2015o http://www.youtube.com/watch?v=fU9hR3kiOK0 (VIDEO)o http://martin.kleppmann.com/2015/03/04/turning-the-database-inside-out.html

(TRANSCRIPT)o http://blog.confluent.io/2015/03/04/turning-the-database-inside-out-with-apach

e-samza/ (BLOG)

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Flink is the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases:

Real-Time stream processing Machine Learning at scale

Graph AnalysisBatch Processing

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2. Why Flink? - Alternative to MapReduce

1. Flink offers cyclic dataflows compared to the two-stage, disk-based MapReduce paradigm.

2. The Application Programming Interface (API) for Flink is easier to use than programming for Hadoop’s MapReduce.

3. Flink is easier to test compared to MapReduce.4. Flink can leverage in-memory processing, data

streaming and iteration operators for faster data processing speed.

5. Flink can work on file systems other than Hadoop.

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2. Why Flink? - Alternative to MapReduce

6. Flink lets users work in a unified framework allowing to build a single data workflow that leverages, streaming, batch, sql and machine learning for example.

7. Flink can analyze real-time streaming data.8. Flink can process graphs using its own Gelly library.9. Flink can use Machine Learning algorithms from its

own FlinkML library.10. Flink supports interactive queries and iterative

algorithms, not well served by Hadoop MapReduce. 

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2. Why Flink? - Alternative to MapReduce

11. Flink extends MapReduce model with new operators: join, cross, union, iterate, iterate delta, cogroup, … 

Input Map Reduce Output

DataSet DataSetDataSet

Red Join

DataSet Map DataSet

OutputS

Input

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2. Why Flink? - Alternative to Storm

1. Higher Level and easier to use API2. Lower latency

Thanks to pipelined engine3. Exactly-once processing guarantees

Variation of Chandy-Lamport4. Higher throughput

Controllable checkpointing overhead5. Flink Separates application logic from

recoveryCheckpointing interval is just a configuration

parameter

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2. Why Flink? - Alternative to Storm

6. More light-weight fault tolerance strategy7. Stateful operators8. Native support for iterative stream processing. 9. Flink does also support batch processing10. Flink offers Storm compatibility

Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm.

https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html

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2. Why Flink? - Alternative to Storm

‘Twitter Heron: Stream Processing at Scale’ by Twitter or “Why Storm Sucks by Twitter themselves”!! http://dl.acm.org/citation.cfm?id=2742788

Recap of the paper: ‘Twitter Heron: Stream Processing at Scale’ - June 15th , 2015 http://blog.acolyer.org/2015/06/15/twitter-heron-stream-processing-at-scale/

High-throughput, low-latency, and exactly-once stream processing with Apache Flink. The evolution of fault-tolerant streaming architectures and their performance – Kostas Tzoumas, August 5th 2015

http://data-artisans.com/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink/

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2. Why Flink? - Alternative to Spark

1. True Low latency streaming engine Spark’s micro-batches aren’t good enough!Unified batch and real-time streaming in a single

engine2. Native closed-loop iteration operators

Make graph and machine learning applications run much faster

3. Custom memory manager No more frequent Out Of Memory errors!Flink’s own type extraction componentFlink’s own serialization component

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2. Why Flink? - Alternative to Spark

4. Automatic Cost Based Optimizer little re-configuration and little maintenance when

the cluster characteristics change and the data evolves over time

5. Little configuration required 6. Little tuning required 7. Flink has better performance

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1. True low latency streaming engine

Many time-critical applications need to process large streams of live data and provide results in real-time. For example:

Financial Fraud detectionFinancial Stock monitoringAnomaly detectionTraffic management applicationsPatient monitoring Online recommenders

Some claim that 95% of streaming use cases can be handled with micro-batches!? Really!!!

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1. True low latency streaming engine Spark’s micro-batching isn’t good enough!Ted Dunning, Chief Applications Architect at MapR,

talk at the Bay Area Apache Flink Meetup on August 27, 2015

http://www.meetup.com/Bay-Area-Apache-Flink-Meetup/events/224189524/

Ted described several use cases where batch and micro batch processing is not appropriate and described why.  

He also described what a true streaming solution needs to provide for solving these problems.

These use cases were taken from real industrial situations, but the descriptions drove down to technical details as well.

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1. True low latency streaming engine “I would consider stream data analysis to be a major

unique selling proposition for Flink. Due to its pipelined architecture, Flink is a perfect match for big data stream processing in the Apache stack.” – Volker Markl

Ref.: On Apache Flink. Interview with Volker Markl, June 24th 2015 http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/

 Apache Flink uses streams for all workloads: streaming, SQL, micro-batch and batch. Batch is just treated as a finite set of streamed data. This makes Flink the most sophisticated distributed open source Big Data processing engine (not the most mature one yet!).

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2. Iteration OperatorsWhy Iterations? Many Machine Learning and Graph processing algorithms need iterations! For example:

Machine Learning Algorithms Clustering (K-Means, Canopy, …) Gradient descent (Logistic Regression, Matrix

Factorization) Graph Processing Algorithms

Page-Rank, Line-Rank Path algorithms on graphs (shortest paths,

centralities, …) Graph communities / dense sub-components Inference (Belief propagation)

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2. Iteration Operators Flink's API offers two dedicated iteration operations:

Iterate and Delta Iterate. Flink executes programs with iterations as cyclic

data flows: a data flow program (and all its operators) is scheduled just once.

In each iteration, the step function consumes the entire input (the result of the previous iteration, or the initial data set), and computes the next version of the partial solution

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2. Iteration Operators Delta iterations run only on parts of the data that is

changing and can significantly speed up many machine learning and graph algorithms because the work in each iteration decreases as the number of iterations goes on.

Documentation on iterations with Apache Flinkhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html

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2. Iteration Operators

StepStep

Step Step Step

Client

for (int i = 0; i < maxIterations; i++) {

// Execute MapReduce job}

Non-native iterations in Hadoop and Spark are implemented as regular for-loops outside the system.

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2. Iteration Operators

Although Spark caches data across iterations, it still needs to schedule and execute a new set of tasks for each iteration.

Spinning Fast Iterative Data Flows - Ewen et al. 2012 : http://vldb.org/pvldb/vol5/p1268_stephanewen_vldb2012.pdf The Apache Flink model for incremental iterative dataflow processing. Academic paper.

Recap of the paper, June 18, 2015http://blog.acolyer.org/2015/06/18/spinning-fast-iterative-dataflows/

Documentation on iterations with Apache Flinkhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html

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3. Custom Memory Manager Features:

C++ style memory management inside the JVM User data stored in serialized byte arrays in JVM Memory is allocated, de-allocated, and used strictly

using an internal buffer pool implementation. Advantages:

1. Flink will not throw an OOM exception on you.2. Reduction of Garbage Collection (GC)3. Very efficient disk spilling and network transfers4. No Need for runtime tuning5. More reliable and stable performance

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3. Custom Memory Manager

public class WC {public String

word; public int count;}

emptypage

Pool of Memory Pages

Sorting, hashing, caching

Shuffles/ broadcasts

User code objects

Man

aged

Unm

anag

edFlink contains its own memory management stack. To do that, Flink contains its own type extraction and serialization components.

JVM Heap

Net

wor

k B

uffe

rs

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3. Custom Memory ManagerPeeking into Apache Flink's Engine Room - by Fabian

Hüske, March 13, 2015 http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html

Juggling with Bits and Bytes - by Fabian Hüske, May 11,2015

https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.htmlMemory Management (Batch API) by Stephan Ewen-

May 16, 2015https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525

Flink added an Off-Heap option for its memory management component in Flink 0.10: https://issues.apache.org/jira/browse/FLINK-1320

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3. Custom Memory Manager

Compared to Flink, Spark is still behind in custom memory management but is catching up with its project Tungsten for Memory Management and Binary Processing: manage memory explicitly and eliminate the overhead of JVM object model and garbage collection. April 28, 2014https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html

It seems that Spark is adopting something similar to Flink and the initial Tungsten announcement read almost like Flink documentation!!

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4. Built-in Cost-Based Optimizer Apache Flink comes with an optimizer that is

independent of the actual programming interface. It chooses a fitting execution strategy depending

on the inputs and operations. Example: the "Join" operator will choose between

partitioning and broadcasting the data, as well as between running a sort-merge-join or a hybrid hash join algorithm.

This helps you focus on your application logic rather than parallel execution.

Quick introduction to the Optimizer: section 6 of the paper: ‘The Stratosphere platform for big data analytics’http://stratosphere.eu/assets/papers/2014-VLDBJ_Stratosphere_Overview.pdf

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4. Built-in Cost-Based Optimizer

Run locally on a data sampleon the laptop

Run a month laterafter the data evolved

Hash vs. SortPartition vs. BroadcastCachingReusing partition/sortExecution

Plan A

ExecutionPlan B

Run on large fileson the cluster

ExecutionPlan C

What is Automatic Optimization? The system's built-in optimizer takes care of finding the best way to execute the program in any environment.

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4. Built-in Cost-Based Optimizer In contrast to Flink’s built-in automatic optimization,

Spark jobs have to be manually optimized and adapted to specific datasets because you need to manually control partitioning and caching if you want to get it right.

Spark SQL uses the Catalyst optimizer that supports both rule-based and cost-based optimization. References:

Spark SQL: Relational Data Processing in Sparkhttp://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf

Deep Dive into Spark SQL’s Catalyst Optimizer https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html

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5. Little configuration required Flink requires no memory thresholds to

configure Flink manages its own memory

Flink requires no complicated network configurations Pipelining engine requires much less

memory for data exchange Flink requires no serializers to be configuredFlink handles its own type extraction and

data representation

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6. Little tuning requiredFlink programs can be adjusted to data

automaticallyFlink’s optimizer can choose execution

strategies automatically According to Mike Olsen, Chief Strategy

Officer of Cloudera Inc. “Spark is too knobby — it has too many tuning parameters, and they need constant adjustment as workloads, data volumes, user counts change.”

Reference: http://vision.cloudera.com/one-platform/

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7. Flink has better performance

Why Flink provides a better performance? Custom memory managerNative closed-loop iteration operators make graph

and machine learning applications run much faster.Role of the built-in automatic optimizer. For

example: more efficient join processing.Pipelining data to the next operator in Flink is more

efficient than in Spark. See benchmarking results against Flink here:

http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87

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Agenda

1. What is Apache Flink?2. Why Apache Flink? 3. How Apache Flink is used at Capital

One?4. Where to learn more about Apache

Flink?5. What are some key takeaways?

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3. How Apache Flink is used at Capital One?We started our journey with Apache Flink at Capital

One while researching and contrasting stream processing tools in the Hadoop ecosystem with a particular interest in the ones providing real-time stream processing capabilities and not just micro-batching as in Apache Spark.

While learning more about Apache Flink, we discovered some unique capabilities of Flink which differentiate it from other Big Data analytics tools not only for Real-Time streaming but also for Batch processing.

We are currently evaluating Apache Flink capabilities in a POC.

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3. How Apache Flink is used at Capital One?Where are we in our Flink journey?

Successful installation of Apache Flink 0.9 in testing Zone of our Pre-Production cluster running on CDH 5.4 with security and High Availability enabled.

Successful installation of Apache Flink 0.9 in a 10 nodes R&D cluster running HDP.

We are currently working on a POC using Flink for a real-time stream processing. The POC will prove that costly Splunk capabilities can be replaced by a combination of tools: Apache Kafka, Apache Flink and Elasticsearch (Kibana, Watcher).

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3. How Apache Flink is used at Capital One?

What are the opportunities for using Apache Flink at Capital One?1. Real-Time stream analytics after

successful conduction of our streaming POC

2. Cascading on Flink3. Flink’s MapReduce Compatibility Layer 4. Flink’s Storm Compatibility Layer 5. Other Flink libraries (Machine Learning

and Graph processing) once they come out of beta.

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3. How Apache Flink is used at Capital One?Cascading on Flink:

First release of Cascading on Flink is being announced soon by Data Artisans and Concurrent. It will be supported in upcoming Cascading 3.1.

Capital One will be the first company to verify this release on real-world Cascading data flows with a simple configuration switch and no code re-work needed!

This is a good example of doing analytics on bounded data sets (Cascading) using a stream processor (Flink)

Expected advantages of performance boost and less resource consumption.

Future work is to support ‘Driven’ from Concurrent Inc. to provide performance management for Cascading data flows running on Flink.

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3. How Apache Flink is used at Capital One? Flink’s DataStream API 0.10 will be released soon and

Flink 1.0 GA will be at the end of 2015 / beginning of 2016.

Flink’s compatibility layer for Storm:We can execute existing Storm topologies using

Flink as the underlying engine.We can reuse our application code (bolts and

spouts) inside Flink programs.  Flink’s libraries (FlinkML for Machine Learning and

Gelly for Large scale graph processing) can be used along Flink’s DataStream API and DataSet API for our end to end big data analytics needs.

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Agenda

1. What is Apache Flink?2. Why Apache Flink? 3. How Apache Flink is used at Capital

One?4. Where to learn more about Apache

Flink?5. What are some key takeaways?

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4. Where to learn more about Flink?

To get an Overview of Apache Flink: http://www.slideshare.net/sbaltagi/overview-of-

apacheflinkbyslimbaltagiTo get started with your first Flink project: Apache Flink Crash Coursehttp://www.slideshare.net/sbaltagi/apache-flinkcrashcoursebyslimbaltagiandsrinipalthepuFree Flink Training from Data Artisans http://dataartisans.github.io/flink-training/

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4. Where to learn more about Flink?

50% off Discount Code: FlinkMeetupWashington50Consider attending the first dedicated Apache Flink

conference on October 12-13, 2015 in Berlin, Germany! http://flink-forward.org/

Two parallel tracks: Talks: Presentations and use cases Trainings: 2 days of hands on training workshops

by the Flink committers

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Agenda

1. What is Apache Flink?2. Why Apache Flink? 3. How Apache Flink is used at Capital

One?4. Where to learn more about Apache

Flink?5. What are some key takeaways?

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5. What are some key takeaways?

1. Although most of the current buzz is about Spark, Flink offers the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases.

2. I foresee more maturity of Apache Flink and more adoption especially in use cases with Real-Time stream processing and also fast iterative machine learning or graph processing.

3. I foresee Flink embedded in major Hadoop distributions and supported!

4. Apache Spark and Apache Flink will both have their sweet spots despite their “Me Too Syndrome”!

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Thanks!To all of you for attending and/or reading the

slides of my talk!To Capital One for hosting and sponsoring

the first Apache Flink Meetup in the DC Area.http://www.meetup.com/Washington-DC-Area-Apache-Flink-Meetup/

Capital One is hiring in Northern Virginia and other locations!

Please check jobs.capitalone.com and search on #ilovedata